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SONIA LONGO

Optimal Design of Grid Integration of a Net Zero Energy Building trough Electrochemical Energy Storage and Fuel Cell Utilization

  • Autori: Aloisio, Davide; Ferraro, Marco; Sergi, Francesco; Brunaccini, Giovanni; Randazzo, Nicola; Tumminia, Giovanni; Longo, Sonia; Guarino, Francesco; Cellura, Maurizio; Antonucci, Vincenzo
  • Anno di pubblicazione: 2018
  • Tipologia: Abstract in atti di convegno pubblicato in volume
  • OA Link: http://hdl.handle.net/10447/294262

Abstract

The integration of electrochemical energy storage, renewable energy production, and fuel cell systems can play a key role in the development of more efficient eco-friendly systems, spanning all sectors of energy management, from stationary to mobile. In particular, residential sector consumed 19% of worldwide energy production in 2015, resulting the third energivorous sector after transport and industry. Distributed energy systems, which efficiently use local resources, can reduce problems in regions with lack of a stable network and more in general help the growth of a sustainable development. In this case, the impact of PV-Lithium Batteries-SOFC integration in a NZEB (Net Zero Energy Builiding) is investigated by using software simulation, developed in Matlab/Simulink environment, to find optimal compromise between environment benefits, electricity grid independence, and costs. The NZEB was realized at CNR-ITAE laboratories by using eco-friendly technologies such as low environmental impact renewable FRP (Fiber Reinforced Pultruded) materials for its manufacturing, high efficiency PV panels, custom innovative multi-source inverter, advanced LiFeMgPO4 batteries, Solid Oxide Fuel Cell, optimized HVAC (Heating, Ventilation and Air Conditioning). Software simulation helps to evaluate and forecast the impact of all of the systems described upon, aiming to obtain an efficient energy management. At first, a monitoring campaign has been launched to evaluate loads consumption, PV production during the year and performances of single elements. After this, all data collected have been used to create models of all the systems present inside the building. The model developed permits to evaluate improvement parameters such as CO2 reduction, Load Cover Factor, Supply Cover Factor, estimate effective energy consumption and costs, and choose the right algorithm for energy management.